{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":2,"outputs":[{"output_type":"stream","text":"/kaggle/input/weekenddd20/Participants_Data_WH20/Test.csv\n/kaggle/input/weekenddd20/Participants_Data_WH20/sample submission.csv\n/kaggle/input/weekenddd20/Participants_Data_WH20/Train.csv\n","name":"stdout"}]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train=pd.read_csv('/kaggle/input/weekenddd20/Participants_Data_WH20/Train.csv')\ntest=pd.read_csv('/kaggle/input/weekenddd20/Participants_Data_WH20/Test.csv')","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Dropping duplicate rows\ntrain=train.drop_duplicates()","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Checking Null values for each column\ntrain.isnull().sum()","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"Labels      0\nText        0\nText_Tag    2\ndtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# There are only two null values , so i am going to drop the null values.\ntrain=train.dropna()","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import nltk\n!pip install sentence-transformers","execution_count":7,"outputs":[{"output_type":"stream","text":"Collecting sentence-transformers\n  Downloading sentence-transformers-0.3.6.tar.gz (62 kB)\n\u001b[K     |████████████████████████████████| 62 kB 474 kB/s eta 0:00:011\n\u001b[?25hCollecting transformers<3.2.0,>=3.1.0\n  Downloading transformers-3.1.0-py3-none-any.whl (884 kB)\n\u001b[K     |████████████████████████████████| 884 kB 3.3 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (4.45.0)\nRequirement already satisfied: torch>=1.2.0 in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (1.5.1)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (1.18.5)\nRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (0.23.2)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (1.4.1)\nRequirement already satisfied: nltk in /opt/conda/lib/python3.7/site-packages (from sentence-transformers) (3.2.4)\nRequirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers<3.2.0,>=3.1.0->sentence-transformers) (2020.4.4)\nRequirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from transformers<3.2.0,>=3.1.0->sentence-transformers) (2.23.0)\nCollecting tokenizers==0.8.1.rc2\n  Downloading tokenizers-0.8.1rc2-cp37-cp37m-manylinux1_x86_64.whl (3.0 MB)\n\u001b[K     |████████████████████████████████| 3.0 MB 8.1 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: sentencepiece!=0.1.92 in /opt/conda/lib/python3.7/site-packages (from transformers<3.2.0,>=3.1.0->sentence-transformers) (0.1.91)\nRequirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from transformers<3.2.0,>=3.1.0->sentence-transformers) (20.1)\nRequirement already satisfied: sacremoses in /opt/conda/lib/python3.7/site-packages (from transformers<3.2.0,>=3.1.0->sentence-transformers) (0.0.43)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from transformers<3.2.0,>=3.1.0->sentence-transformers) (3.0.10)\nRequirement already satisfied: future in /opt/conda/lib/python3.7/site-packages (from torch>=1.2.0->sentence-transformers) (0.18.2)\nRequirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->sentence-transformers) (0.14.1)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->sentence-transformers) (2.1.0)\nRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from nltk->sentence-transformers) (1.14.0)\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->transformers<3.2.0,>=3.1.0->sentence-transformers) (2.9)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers<3.2.0,>=3.1.0->sentence-transformers) (2020.6.20)\nRequirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->transformers<3.2.0,>=3.1.0->sentence-transformers) (3.0.4)\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->transformers<3.2.0,>=3.1.0->sentence-transformers) (1.24.3)\nRequirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->transformers<3.2.0,>=3.1.0->sentence-transformers) (2.4.7)\nRequirement already satisfied: click in /opt/conda/lib/python3.7/site-packages (from sacremoses->transformers<3.2.0,>=3.1.0->sentence-transformers) (7.1.1)\nBuilding wheels for collected packages: sentence-transformers\n  Building wheel for sentence-transformers (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for sentence-transformers: filename=sentence_transformers-0.3.6-py3-none-any.whl size=101181 sha256=d23638cb0fb1fffc369f1341cb8688e0189930f394dbe09a8f208a8d4e2b0270\n  Stored in directory: /root/.cache/pip/wheels/b5/94/b4/953a1fd26652702c88112a188346df4cead56f0e3971a6d653\nSuccessfully built sentence-transformers\nInstalling collected packages: tokenizers, transformers, sentence-transformers\n  Attempting uninstall: tokenizers\n    Found existing installation: tokenizers 0.7.0\n    Uninstalling tokenizers-0.7.0:\n      Successfully uninstalled tokenizers-0.7.0\n  Attempting uninstall: transformers\n    Found existing installation: transformers 2.11.0\n    Uninstalling transformers-2.11.0:\n      Successfully uninstalled transformers-2.11.0\n\u001b[31mERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.\n\nWe recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\n\nallennlp 1.0.0 requires transformers<2.12,>=2.9, but you'll have transformers 3.1.0 which is incompatible.\u001b[0m\nSuccessfully installed sentence-transformers-0.3.6 tokenizers-0.8.1rc2 transformers-3.1.0\n\u001b[33mWARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.\nYou should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sentence_transformers import SentenceTransformer","execution_count":8,"outputs":[{"output_type":"stream","text":"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m W&B installed but not logged in.  Run `wandb login` or set the WANDB_API_KEY env variable.\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"embedder = SentenceTransformer('roberta-large-nli-stsb-mean-tokens')","execution_count":9,"outputs":[{"output_type":"stream","text":"100%|██████████| 1.31G/1.31G [04:13<00:00, 5.18MB/s] \n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"full_df =pd.concat([train, test]).reset_index()","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"full_df['Length'] = full_df['Text'].apply(len)\nfull_df['Length1'] = full_df['Text_Tag'].apply(len)","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"word_embeddings = embedder.encode(full_df.Text)","execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, description='Batches', max=360.0, style=ProgressStyle(description_widt…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b67197862c4247dfa3f15b6ceecf6460"}},"metadata":{}},{"output_type":"stream","text":"\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = pd.DataFrame(word_embeddings)\ndata.head()","execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"       0         1         2         3         4         5         6     \\\n0 -0.843116  0.354265  0.356840  0.419612  0.502844  0.265882  1.124244   \n1 -0.154687  0.872684  1.772495 -0.295068  0.222306 -0.253727 -1.300732   \n2 -1.090438 -1.039242 -0.442930 -1.469321  0.165641 -0.411884 -0.524986   \n3  0.599439  0.623602  0.633820  0.405360  0.706751 -0.079209  0.344766   \n4 -0.092456 -0.040649 -0.215230  0.439570  1.114174  0.060738 -0.115374   \n\n       7         8         9     ...      1014      1015      1016      1017  \\\n0 -0.337540 -1.011598  0.389833  ...  0.022141  0.510810  0.853409  0.351011   \n1  0.190506  0.343303 -0.872295  ... -0.493620  0.729953 -0.513199  1.092360   \n2  1.435754 -0.221386  0.336803  ...  0.896936  1.188237 -0.614642  0.053428   \n3 -0.220472 -0.569685 -0.079065  ...  0.311481  0.002133 -0.285264  0.073709   \n4 -0.424731  0.706268 -0.155484  ... -0.951390 -0.129189 -0.908807  1.449807   \n\n       1018      1019      1020      1021      1022      1023  \n0  0.043097  0.612750 -0.362310  0.728116 -0.631632 -0.265538  \n1 -0.258280  0.267085 -0.399993 -0.463843 -1.466223  0.220742  \n2 -0.562576  0.325781 -1.103370 -1.954508  0.481244 -0.146409  \n3  0.787636 -0.344154 -0.042825  1.363382  0.595174 -0.336082  \n4  0.192440  0.047139 -0.335581  0.606919 -0.808966 -0.328456  \n\n[5 rows x 1024 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>1014</th>\n      <th>1015</th>\n      <th>1016</th>\n      <th>1017</th>\n      <th>1018</th>\n      <th>1019</th>\n      <th>1020</th>\n      <th>1021</th>\n      <th>1022</th>\n      <th>1023</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-0.843116</td>\n      <td>0.354265</td>\n      <td>0.356840</td>\n      <td>0.419612</td>\n      <td>0.502844</td>\n      <td>0.265882</td>\n      <td>1.124244</td>\n      <td>-0.337540</td>\n      <td>-1.011598</td>\n      <td>0.389833</td>\n      <td>...</td>\n      <td>0.022141</td>\n      <td>0.510810</td>\n      <td>0.853409</td>\n      <td>0.351011</td>\n      <td>0.043097</td>\n      <td>0.612750</td>\n      <td>-0.362310</td>\n      <td>0.728116</td>\n      <td>-0.631632</td>\n      <td>-0.265538</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.154687</td>\n      <td>0.872684</td>\n      <td>1.772495</td>\n      <td>-0.295068</td>\n      <td>0.222306</td>\n      <td>-0.253727</td>\n      <td>-1.300732</td>\n      <td>0.190506</td>\n      <td>0.343303</td>\n      <td>-0.872295</td>\n      <td>...</td>\n      <td>-0.493620</td>\n      <td>0.729953</td>\n      <td>-0.513199</td>\n      <td>1.092360</td>\n      <td>-0.258280</td>\n      <td>0.267085</td>\n      <td>-0.399993</td>\n      <td>-0.463843</td>\n      <td>-1.466223</td>\n      <td>0.220742</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-1.090438</td>\n      <td>-1.039242</td>\n      <td>-0.442930</td>\n      <td>-1.469321</td>\n      <td>0.165641</td>\n      <td>-0.411884</td>\n      <td>-0.524986</td>\n      <td>1.435754</td>\n      <td>-0.221386</td>\n      <td>0.336803</td>\n      <td>...</td>\n      <td>0.896936</td>\n      <td>1.188237</td>\n      <td>-0.614642</td>\n      <td>0.053428</td>\n      <td>-0.562576</td>\n      <td>0.325781</td>\n      <td>-1.103370</td>\n      <td>-1.954508</td>\n      <td>0.481244</td>\n      <td>-0.146409</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.599439</td>\n      <td>0.623602</td>\n      <td>0.633820</td>\n      <td>0.405360</td>\n      <td>0.706751</td>\n      <td>-0.079209</td>\n      <td>0.344766</td>\n      <td>-0.220472</td>\n      <td>-0.569685</td>\n      <td>-0.079065</td>\n      <td>...</td>\n      <td>0.311481</td>\n      <td>0.002133</td>\n      <td>-0.285264</td>\n      <td>0.073709</td>\n      <td>0.787636</td>\n      <td>-0.344154</td>\n      <td>-0.042825</td>\n      <td>1.363382</td>\n      <td>0.595174</td>\n      <td>-0.336082</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-0.092456</td>\n      <td>-0.040649</td>\n      <td>-0.215230</td>\n      <td>0.439570</td>\n      <td>1.114174</td>\n      <td>0.060738</td>\n      <td>-0.115374</td>\n      <td>-0.424731</td>\n      <td>0.706268</td>\n      <td>-0.155484</td>\n      <td>...</td>\n      <td>-0.951390</td>\n      <td>-0.129189</td>\n      <td>-0.908807</td>\n      <td>1.449807</td>\n      <td>0.192440</td>\n      <td>0.047139</td>\n      <td>-0.335581</td>\n      <td>0.606919</td>\n      <td>-0.808966</td>\n      <td>-0.328456</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 1024 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"norm_train_reviews =full_df.Text_Tag.values","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# I applied countvectorizer on Text_Tag column\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n\n# build BOW features on train reviews\ncv = CountVectorizer()\ncv_train_features = cv.fit_transform(norm_train_reviews)\n\n\n# build TFIDF features on train reviews\ntv = TfidfVectorizer()\ntv_train_features = tv.fit_transform(norm_train_reviews)","execution_count":15,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cv_matrix = cv_train_features.toarray()\n\nvocab = cv.get_feature_names()\ncv=pd.DataFrame(cv_matrix, columns=vocab)","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cv.head()","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"   10  2012  2014  abc  abortion  accomplishments  administration  \\\n0   0     0     0    0         1                0               0   \n1   0     0     0    0         0                1               0   \n2   0     0     0    0         0                0               0   \n3   0     0     0    0         0                0               0   \n4   0     0     0    0         0                0               0   \n\n   advertising  afghanistan  after  ...  veterans  voting  wall  water  \\\n0            0            0      0  ...         0       0     0      0   \n1            0            0      0  ...         0       0     0      0   \n2            0            0      0  ...         0       0     0      0   \n3            0            0      0  ...         0       0     0      0   \n4            0            0      0  ...         0       0     0      0   \n\n   wealth  weather  week  welfare  women  workers  \n0       0        0     0        0      0        0  \n1       0        0     0        0      0        0  \n2       0        0     0        0      0        0  \n3       0        0     0        0      0        0  \n4       0        0     0        0      0        0  \n\n[5 rows x 182 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>10</th>\n      <th>2012</th>\n      <th>2014</th>\n      <th>abc</th>\n      <th>abortion</th>\n      <th>accomplishments</th>\n      <th>administration</th>\n      <th>advertising</th>\n      <th>afghanistan</th>\n      <th>after</th>\n      <th>...</th>\n      <th>veterans</th>\n      <th>voting</th>\n      <th>wall</th>\n      <th>water</th>\n      <th>wealth</th>\n      <th>weather</th>\n      <th>week</th>\n      <th>welfare</th>\n      <th>women</th>\n      <th>workers</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 182 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"cv1=cv.drop(['10'],axis=1)","execution_count":18,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data12=pd.concat([data,cv1],axis=1)\ndata12['Labels']=full_df.Labels.values\ndata12['Length']=full_df.Length.values\ndata12['Length1']=full_df.Length1.values\ndata12.head()","execution_count":19,"outputs":[{"output_type":"execute_result","execution_count":19,"data":{"text/plain":"          0         1         2         3         4         5         6  \\\n0 -0.843116  0.354265  0.356840  0.419612  0.502844  0.265882  1.124244   \n1 -0.154687  0.872684  1.772495 -0.295068  0.222306 -0.253727 -1.300732   \n2 -1.090438 -1.039242 -0.442930 -1.469321  0.165641 -0.411884 -0.524986   \n3  0.599439  0.623602  0.633820  0.405360  0.706751 -0.079209  0.344766   \n4 -0.092456 -0.040649 -0.215230  0.439570  1.114174  0.060738 -0.115374   \n\n          7         8         9  ...  water  wealth  weather  week  welfare  \\\n0 -0.337540 -1.011598  0.389833  ...      0       0        0     0        0   \n1  0.190506  0.343303 -0.872295  ...      0       0        0     0        0   \n2  1.435754 -0.221386  0.336803  ...      0       0        0     0        0   \n3 -0.220472 -0.569685 -0.079065  ...      0       0        0     0        0   \n4 -0.424731  0.706268 -0.155484  ...      0       0        0     0        0   \n\n   women  workers  Labels  Length  Length1  \n0      0        0     1.0      82        8  \n1      0        0     2.0     141       34  \n2      0        0     3.0     105       14  \n3      0        0     1.0      78       11  \n4      0        0     2.0      54       12  \n\n[5 rows x 1208 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>water</th>\n      <th>wealth</th>\n      <th>weather</th>\n      <th>week</th>\n      <th>welfare</th>\n      <th>women</th>\n      <th>workers</th>\n      <th>Labels</th>\n      <th>Length</th>\n      <th>Length1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-0.843116</td>\n      <td>0.354265</td>\n      <td>0.356840</td>\n      <td>0.419612</td>\n      <td>0.502844</td>\n      <td>0.265882</td>\n      <td>1.124244</td>\n      <td>-0.337540</td>\n      <td>-1.011598</td>\n      <td>0.389833</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.0</td>\n      <td>82</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.154687</td>\n      <td>0.872684</td>\n      <td>1.772495</td>\n      <td>-0.295068</td>\n      <td>0.222306</td>\n      <td>-0.253727</td>\n      <td>-1.300732</td>\n      <td>0.190506</td>\n      <td>0.343303</td>\n      <td>-0.872295</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>141</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-1.090438</td>\n      <td>-1.039242</td>\n      <td>-0.442930</td>\n      <td>-1.469321</td>\n      <td>0.165641</td>\n      <td>-0.411884</td>\n      <td>-0.524986</td>\n      <td>1.435754</td>\n      <td>-0.221386</td>\n      <td>0.336803</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.0</td>\n      <td>105</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.599439</td>\n      <td>0.623602</td>\n      <td>0.633820</td>\n      <td>0.405360</td>\n      <td>0.706751</td>\n      <td>-0.079209</td>\n      <td>0.344766</td>\n      <td>-0.220472</td>\n      <td>-0.569685</td>\n      <td>-0.079065</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.0</td>\n      <td>78</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-0.092456</td>\n      <td>-0.040649</td>\n      <td>-0.215230</td>\n      <td>0.439570</td>\n      <td>1.114174</td>\n      <td>0.060738</td>\n      <td>-0.115374</td>\n      <td>-0.424731</td>\n      <td>0.706268</td>\n      <td>-0.155484</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>54</td>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 1208 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train1 = data12[~data12.Labels.isna()]\ntest1 = data12[data12.Labels.isna()]\ntest1.drop(\"Labels\",axis=1,inplace=True)","execution_count":20,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:4167: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  errors=errors,\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=train1.drop(['Labels'],axis=1)\ny=train1['Labels']","execution_count":23,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split","execution_count":24,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)","execution_count":25,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from catboost import CatBoostClassifier\nmodel=CatBoostClassifier(iterations=1000,task_type='GPU')","execution_count":26,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import lightgbm as lgb\nlgbm=lgb.LGBMClassifier(n_estimators=100,device=\"gpu\",max_bin=25)","execution_count":27,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.ensemble import VotingClassifier\nvclf=VotingClassifier(estimators=[('lgg',lgbm),('xgg',model)],voting='soft')","execution_count":28,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"vclf.fit(X_train,y_train)","execution_count":29,"outputs":[{"output_type":"stream","text":"Learning rate set 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voting='soft')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"prediction=vclf.predict_proba(test1)","execution_count":30,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Results are using Robert large sentence transformer\nresults=pd.DataFrame(prediction, columns=[0,1,2,3,4,5])","execution_count":31,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"embedder1 = SentenceTransformer('roberta-base-nli-stsb-mean-tokens')","execution_count":32,"outputs":[{"output_type":"stream","text":"100%|██████████| 459M/459M [00:19<00:00, 24.0MB/s] \n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nbase_embeddings=embedder1.encode(full_df.Text.values.tolist(),batch_size=128,show_progress_bar=True)","execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, description='Batches', max=90.0, style=ProgressStyle(description_width…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"706c5086f9054d30800504823a02073b"}},"metadata":{}},{"output_type":"stream","text":"\nCPU times: user 10.4 s, sys: 1.38 s, total: 11.8 s\nWall time: 11.9 s\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data= pd.DataFrame(base_embeddings)\ndata.head()","execution_count":34,"outputs":[{"output_type":"execute_result","execution_count":34,"data":{"text/plain":"        0         1         2         3         4         5         6    \\\n0  0.063559  0.073300 -0.032389  1.328562 -0.049966  0.453855  0.628987   \n1 -0.251282 -0.427436 -0.002071 -1.095850 -0.286984 -0.405636  0.545493   \n2  0.573638  0.121656 -0.146542  0.829190 -0.009897 -0.721362  0.463740   \n3  1.256146  0.762251  0.183387 -0.102740  0.100235  0.485516  0.039702   \n4 -0.105395  0.239276  0.312050 -0.679842 -0.468682 -0.453707  0.445754   \n\n        7         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<td>0.707739</td>\n      <td>0.125505</td>\n      <td>0.349462</td>\n      <td>-0.382211</td>\n      <td>1.345453</td>\n      <td>0.671241</td>\n      <td>-0.090033</td>\n      <td>-0.924622</td>\n      <td>0.593463</td>\n      <td>-0.151931</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.251282</td>\n      <td>-0.427436</td>\n      <td>-0.002071</td>\n      <td>-1.095850</td>\n      <td>-0.286984</td>\n      <td>-0.405636</td>\n      <td>0.545493</td>\n      <td>-0.388819</td>\n      <td>0.524717</td>\n      <td>0.264293</td>\n      <td>...</td>\n      <td>0.449813</td>\n      <td>-1.002035</td>\n      <td>-0.211106</td>\n      <td>0.256451</td>\n      <td>-0.323354</td>\n      <td>0.298147</td>\n      <td>0.432782</td>\n      <td>-1.182147</td>\n      <td>0.014275</td>\n      <td>-0.618577</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.573638</td>\n      <td>0.121656</td>\n      <td>-0.146542</td>\n      <td>0.829190</td>\n      <td>-0.009897</td>\n      <td>-0.721362</td>\n      <td>0.463740</td>\n      <td>0.480717</td>\n      <td>1.374566</td>\n      <td>-0.134241</td>\n      <td>...</td>\n      <td>-0.635795</td>\n      <td>-0.543450</td>\n      <td>-1.251009</td>\n      <td>0.389167</td>\n      <td>0.127668</td>\n      <td>-0.334683</td>\n      <td>0.480950</td>\n      <td>0.778091</td>\n      <td>-0.727586</td>\n      <td>0.076265</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.256146</td>\n      <td>0.762251</td>\n      <td>0.183387</td>\n      <td>-0.102740</td>\n      <td>0.100235</td>\n      <td>0.485516</td>\n      <td>0.039702</td>\n      <td>-0.064118</td>\n      <td>-0.129972</td>\n      <td>-0.187275</td>\n      <td>...</td>\n      <td>0.355464</td>\n      <td>-0.488293</td>\n      <td>0.624699</td>\n      <td>-0.668803</td>\n      <td>0.678824</td>\n      <td>0.429867</td>\n      <td>0.099317</td>\n      <td>-1.353077</td>\n      <td>0.240512</td>\n      <td>-0.293187</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-0.105395</td>\n      <td>0.239276</td>\n      <td>0.312050</td>\n      <td>-0.679842</td>\n      <td>-0.468682</td>\n      <td>-0.453707</td>\n      <td>0.445754</td>\n      <td>-0.465910</td>\n      <td>0.302358</td>\n      <td>0.581789</td>\n      <td>...</td>\n      <td>-0.121204</td>\n      <td>-0.133457</td>\n      <td>-0.121812</td>\n      <td>1.138613</td>\n      <td>0.084732</td>\n      <td>0.470364</td>\n      <td>-0.812005</td>\n      <td>0.102160</td>\n      <td>0.069002</td>\n      <td>-0.161689</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 768 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data12=pd.concat([data,cv1],axis=1)\ndata12['Labels']=full_df.Labels.values\ndata12['Length']=full_df.Length.values\ndata12['Length1']=full_df.Length1.values\ndata12.head()","execution_count":35,"outputs":[{"output_type":"execute_result","execution_count":35,"data":{"text/plain":"          0         1         2         3         4         5         6  \\\n0  0.063559  0.073300 -0.032389  1.328562 -0.049966  0.453855  0.628987   \n1 -0.251282 -0.427436 -0.002071 -1.095850 -0.286984 -0.405636  0.545493   \n2  0.573638  0.121656 -0.146542  0.829190 -0.009897 -0.721362  0.463740   \n3  1.256146  0.762251  0.183387 -0.102740  0.100235  0.485516  0.039702   \n4 -0.105395  0.239276  0.312050 -0.679842 -0.468682 -0.453707  0.445754   \n\n          7         8         9  ...  water  wealth  weather  week  welfare  \\\n0  0.656944  0.336365 -0.718979  ...      0       0        0     0        0   \n1 -0.388819  0.524717  0.264293  ...      0       0        0     0        0   \n2  0.480717  1.374566 -0.134241  ...      0       0        0     0        0   \n3 -0.064118 -0.129972 -0.187275  ...      0       0        0     0        0   \n4 -0.465910  0.302358  0.581789  ...      0       0        0     0        0   \n\n   women  workers  Labels  Length  Length1  \n0      0        0     1.0      82        8  \n1      0        0     2.0     141       34  \n2      0        0     3.0     105       14  \n3      0        0     1.0      78       11  \n4      0        0     2.0      54       12  \n\n[5 rows x 952 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>water</th>\n      <th>wealth</th>\n      <th>weather</th>\n      <th>week</th>\n      <th>welfare</th>\n      <th>women</th>\n      <th>workers</th>\n      <th>Labels</th>\n      <th>Length</th>\n      <th>Length1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.063559</td>\n      <td>0.073300</td>\n      <td>-0.032389</td>\n      <td>1.328562</td>\n      <td>-0.049966</td>\n      <td>0.453855</td>\n      <td>0.628987</td>\n      <td>0.656944</td>\n      <td>0.336365</td>\n      <td>-0.718979</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.0</td>\n      <td>82</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.251282</td>\n      <td>-0.427436</td>\n      <td>-0.002071</td>\n      <td>-1.095850</td>\n      <td>-0.286984</td>\n      <td>-0.405636</td>\n      <td>0.545493</td>\n      <td>-0.388819</td>\n      <td>0.524717</td>\n      <td>0.264293</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>141</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.573638</td>\n      <td>0.121656</td>\n      <td>-0.146542</td>\n      <td>0.829190</td>\n      <td>-0.009897</td>\n      <td>-0.721362</td>\n      <td>0.463740</td>\n      <td>0.480717</td>\n      <td>1.374566</td>\n      <td>-0.134241</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.0</td>\n      <td>105</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.256146</td>\n      <td>0.762251</td>\n      <td>0.183387</td>\n      <td>-0.102740</td>\n      <td>0.100235</td>\n      <td>0.485516</td>\n      <td>0.039702</td>\n      <td>-0.064118</td>\n      <td>-0.129972</td>\n      <td>-0.187275</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.0</td>\n      <td>78</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-0.105395</td>\n      <td>0.239276</td>\n      <td>0.312050</td>\n      <td>-0.679842</td>\n      <td>-0.468682</td>\n      <td>-0.453707</td>\n      <td>0.445754</td>\n      <td>-0.465910</td>\n      <td>0.302358</td>\n      <td>0.581789</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>54</td>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 952 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train1 = data12[~data12.Labels.isna()]\ntest1 = data12[data12.Labels.isna()]\ntest1.drop(\"Labels\",axis=1,inplace=True)","execution_count":36,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:4167: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  errors=errors,\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=train1.drop(['Labels'],axis=1)\ny=train1['Labels']","execution_count":38,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)","execution_count":39,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"vclf.fit(X_train,y_train)","execution_count":40,"outputs":[{"output_type":"stream","text":"Learning rate set to 0.099662\n","name":"stdout"},{"output_type":"stream","text":"Warning: less than 75% gpu memory available for training. 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voting='soft')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"prediction=vclf.predict_proba(test1)","execution_count":41,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Results are using Robert base sentence transformer\nresults1=pd.DataFrame(prediction, columns=[0,1,2,3,4,5])","execution_count":42,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"embedder2 = SentenceTransformer('bert-large-nli-stsb-mean-tokens')","execution_count":43,"outputs":[{"output_type":"stream","text":"100%|██████████| 1.24G/1.24G [00:58<00:00, 21.2MB/s] \n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"word_embeddings1 = embedder2.encode(full_df.Text)","execution_count":44,"outputs":[{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, description='Batches', max=360.0, style=ProgressStyle(description_widt…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b99b7cb995584ccfab7c5149651cbc47"}},"metadata":{}},{"output_type":"stream","text":"\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data=pd.DataFrame(word_embeddings1)\ndata.head()","execution_count":45,"outputs":[{"output_type":"execute_result","execution_count":45,"data":{"text/plain":"       0         1         2         3         4         5         6     \\\n0  0.700180 -0.769513 -0.544782 -0.618669 -0.919392  0.513581  0.670450   \n1 -0.108184 -0.801630  0.147472 -0.389542  0.408265  0.538518  0.736174   \n2 -0.344798 -0.046441  0.021711 -0.201737 -0.174805  1.339390 -0.052499   \n3  0.834027 -0.505015 -0.110948 -0.977177 -0.278406 -0.174256 -0.116903   \n4  0.542017  0.590681 -0.069034 -1.087724 -0.278888  0.354331  0.212271   \n\n       7         8         9     ...      1014      1015      1016      1017  \\\n0  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<td>-0.299323</td>\n      <td>-0.543496</td>\n      <td>0.348448</td>\n      <td>-1.271895</td>\n      <td>-0.134442</td>\n      <td>-1.123390</td>\n      <td>0.452224</td>\n      <td>0.162756</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.108184</td>\n      <td>-0.801630</td>\n      <td>0.147472</td>\n      <td>-0.389542</td>\n      <td>0.408265</td>\n      <td>0.538518</td>\n      <td>0.736174</td>\n      <td>-0.005731</td>\n      <td>-0.525059</td>\n      <td>-0.159115</td>\n      <td>...</td>\n      <td>-0.004297</td>\n      <td>-0.261957</td>\n      <td>-0.488298</td>\n      <td>0.085653</td>\n      <td>-0.105378</td>\n      <td>0.064214</td>\n      <td>1.115160</td>\n      <td>0.133910</td>\n      <td>-0.467474</td>\n      <td>-0.955050</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.344798</td>\n      <td>-0.046441</td>\n      <td>0.021711</td>\n      <td>-0.201737</td>\n      <td>-0.174805</td>\n      <td>1.339390</td>\n      <td>-0.052499</td>\n      <td>0.256828</td>\n      <td>-0.738929</td>\n      <td>0.202591</td>\n      <td>...</td>\n      <td>0.163834</td>\n      <td>0.541816</td>\n      <td>-0.641019</td>\n      <td>-1.710618</td>\n      <td>0.454957</td>\n      <td>-0.250923</td>\n      <td>0.295782</td>\n      <td>-0.131281</td>\n      <td>0.645660</td>\n      <td>0.212365</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.834027</td>\n      <td>-0.505015</td>\n      <td>-0.110948</td>\n      <td>-0.977177</td>\n      <td>-0.278406</td>\n      <td>-0.174256</td>\n      <td>-0.116903</td>\n      <td>0.578716</td>\n      <td>-0.399319</td>\n      <td>-0.425492</td>\n      <td>...</td>\n      <td>0.047619</td>\n      <td>-1.227428</td>\n      <td>-0.076522</td>\n      <td>-0.311735</td>\n      <td>0.856727</td>\n      <td>-0.444029</td>\n      <td>-1.038579</td>\n      <td>-1.068880</td>\n      <td>0.275600</td>\n      <td>0.559146</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.542017</td>\n      <td>0.590681</td>\n      <td>-0.069034</td>\n      <td>-1.087724</td>\n      <td>-0.278888</td>\n      <td>0.354331</td>\n      <td>0.212271</td>\n      <td>0.297291</td>\n      <td>-0.337527</td>\n      <td>-0.086165</td>\n      <td>...</td>\n      <td>-0.170516</td>\n      <td>-0.868259</td>\n      <td>-0.413786</td>\n      <td>0.305759</td>\n      <td>-0.469030</td>\n      <td>-0.374569</td>\n      <td>0.650067</td>\n      <td>0.069905</td>\n      <td>-0.076002</td>\n      <td>0.253064</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 1024 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data12=pd.concat([data,cv1],axis=1)\ndata12['Labels']=full_df.Labels.values\ndata12['Length']=full_df.Length.values\ndata12['Length1']=full_df.Length1.values\ndata12.head()","execution_count":46,"outputs":[{"output_type":"execute_result","execution_count":46,"data":{"text/plain":"          0         1         2         3         4         5         6  \\\n0  0.700180 -0.769513 -0.544782 -0.618669 -0.919392  0.513581  0.670450   \n1 -0.108184 -0.801630  0.147472 -0.389542  0.408265  0.538518  0.736174   \n2 -0.344798 -0.046441  0.021711 -0.201737 -0.174805  1.339390 -0.052499   \n3  0.834027 -0.505015 -0.110948 -0.977177 -0.278406 -0.174256 -0.116903   \n4  0.542017  0.590681 -0.069034 -1.087724 -0.278888  0.354331  0.212271   \n\n          7         8         9  ...  water  wealth  weather  week  welfare  \\\n0  0.689292  0.456358 -0.047067  ...      0       0        0     0        0   \n1 -0.005731 -0.525059 -0.159115  ...      0       0        0     0        0   \n2  0.256828 -0.738929  0.202591  ...      0       0        0     0        0   \n3  0.578716 -0.399319 -0.425492  ...      0       0        0     0        0   \n4  0.297291 -0.337527 -0.086165  ...      0       0        0     0        0   \n\n   women  workers  Labels  Length  Length1  \n0      0        0     1.0      82        8  \n1      0        0     2.0     141       34  \n2      0        0     3.0     105       14  \n3      0        0     1.0      78       11  \n4      0        0     2.0      54       12  \n\n[5 rows x 1208 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>water</th>\n      <th>wealth</th>\n      <th>weather</th>\n      <th>week</th>\n      <th>welfare</th>\n      <th>women</th>\n      <th>workers</th>\n      <th>Labels</th>\n      <th>Length</th>\n      <th>Length1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.700180</td>\n      <td>-0.769513</td>\n      <td>-0.544782</td>\n      <td>-0.618669</td>\n      <td>-0.919392</td>\n      <td>0.513581</td>\n      <td>0.670450</td>\n      <td>0.689292</td>\n      <td>0.456358</td>\n      <td>-0.047067</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.0</td>\n      <td>82</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.108184</td>\n      <td>-0.801630</td>\n      <td>0.147472</td>\n      <td>-0.389542</td>\n      <td>0.408265</td>\n      <td>0.538518</td>\n      <td>0.736174</td>\n      <td>-0.005731</td>\n      <td>-0.525059</td>\n      <td>-0.159115</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>141</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.344798</td>\n      <td>-0.046441</td>\n      <td>0.021711</td>\n      <td>-0.201737</td>\n      <td>-0.174805</td>\n      <td>1.339390</td>\n      <td>-0.052499</td>\n      <td>0.256828</td>\n      <td>-0.738929</td>\n      <td>0.202591</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.0</td>\n      <td>105</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.834027</td>\n      <td>-0.505015</td>\n      <td>-0.110948</td>\n      <td>-0.977177</td>\n      <td>-0.278406</td>\n      <td>-0.174256</td>\n      <td>-0.116903</td>\n      <td>0.578716</td>\n      <td>-0.399319</td>\n      <td>-0.425492</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.0</td>\n      <td>78</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.542017</td>\n      <td>0.590681</td>\n      <td>-0.069034</td>\n      <td>-1.087724</td>\n      <td>-0.278888</td>\n      <td>0.354331</td>\n      <td>0.212271</td>\n      <td>0.297291</td>\n      <td>-0.337527</td>\n      <td>-0.086165</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>54</td>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 1208 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train1 = data12[~data12.Labels.isna()]\ntest1 = data12[data12.Labels.isna()]\ntest1.drop(\"Labels\",axis=1,inplace=True)","execution_count":47,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:4167: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  errors=errors,\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=train1.drop(['Labels'],axis=1)\ny=train1['Labels']","execution_count":48,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)","execution_count":49,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"vclf.fit(X_train,y_train)","execution_count":50,"outputs":[{"output_type":"stream","text":"Learning rate set to 0.099662\n","name":"stdout"},{"output_type":"stream","text":"Warning: less than 75% gpu memory available for training. 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voting='soft')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"prediction=vclf.predict_proba(test1)","execution_count":51,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Results are using bert large sentence transformer\nresults2=pd.DataFrame(prediction, columns=[0,1,2,3,4,5])","execution_count":52,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"embedder3= SentenceTransformer('distilbert-base-nli-stsb-mean-tokens')","execution_count":53,"outputs":[{"output_type":"stream","text":"100%|██████████| 245M/245M [00:12<00:00, 20.3MB/s] \n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nbase_embeddings2=embedder3.encode(full_df.Text.values.tolist(),batch_size=128,show_progress_bar=True)","execution_count":54,"outputs":[{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, description='Batches', max=90.0, style=ProgressStyle(description_width…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5e0c45e7d99c4c3ab780468fdbe62446"}},"metadata":{}},{"output_type":"stream","text":"\nCPU times: user 10.4 s, sys: 173 ms, total: 10.6 s\nWall time: 11 s\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data=pd.DataFrame(base_embeddings2)\ndata.head()","execution_count":55,"outputs":[{"output_type":"execute_result","execution_count":55,"data":{"text/plain":"        0         1         2         3         4         5         6    \\\n0 -0.144368 -0.238294  0.599350  0.149775  0.170297 -0.141637  0.989932   \n1  0.296415 -0.713782 -0.306153 -0.011605  0.344352 -1.188767  0.150497   \n2 -0.440064  0.455458 -0.893356  0.295065  0.160187 -0.527492  0.092013   \n3  0.088425 -0.284115  0.300834 -0.170313 -0.476598 -0.773967  0.929880   \n4 -0.585023 -0.932097  0.649800 -1.510238  0.908814  0.078908 -0.227934   \n\n        7         8         9    ...       758       759       760       761  \\\n0 -0.510203  0.628405  0.283468  ... -1.966928 -1.018470 -0.524895  0.260997   \n1 -0.162290  0.770730 -0.092008  ...  0.748893 -0.861240  0.282171 -0.387093   \n2 -0.255349  0.982621  0.172117  ... -0.485759 -0.251611  0.203046  0.436484   \n3 -0.669233  1.217801 -0.511516  ... -0.428810 -0.814650  0.189426  0.598014   \n4 -0.126206 -0.677687 -0.472893  ... -0.032966 -0.012548  1.557752  0.467225   \n\n        762       763       764       765       766       767  \n0 -0.420094  0.543894  1.047531  0.079142 -0.001416 -0.297475  \n1 -0.145990 -0.689599  0.727035  0.093808  0.613867 -0.156318  \n2  0.584199  0.138801  0.179525  0.117047 -0.514392 -0.121610  \n3 -0.078981  0.344360  0.305921  0.111837 -0.194737  0.286152  \n4  0.275561 -0.823539 -0.176462  0.279183 -0.277266 -0.130289  \n\n[5 rows x 768 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>758</th>\n      <th>759</th>\n      <th>760</th>\n      <th>761</th>\n      <th>762</th>\n      <th>763</th>\n      <th>764</th>\n      <th>765</th>\n      <th>766</th>\n      <th>767</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-0.144368</td>\n      <td>-0.238294</td>\n      <td>0.599350</td>\n      <td>0.149775</td>\n      <td>0.170297</td>\n      <td>-0.141637</td>\n      <td>0.989932</td>\n      <td>-0.510203</td>\n      <td>0.628405</td>\n      <td>0.283468</td>\n      <td>...</td>\n      <td>-1.966928</td>\n      <td>-1.018470</td>\n      <td>-0.524895</td>\n      <td>0.260997</td>\n      <td>-0.420094</td>\n      <td>0.543894</td>\n      <td>1.047531</td>\n      <td>0.079142</td>\n      <td>-0.001416</td>\n      <td>-0.297475</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.296415</td>\n      <td>-0.713782</td>\n      <td>-0.306153</td>\n      <td>-0.011605</td>\n      <td>0.344352</td>\n      <td>-1.188767</td>\n      <td>0.150497</td>\n      <td>-0.162290</td>\n      <td>0.770730</td>\n      <td>-0.092008</td>\n      <td>...</td>\n      <td>0.748893</td>\n      <td>-0.861240</td>\n      <td>0.282171</td>\n      <td>-0.387093</td>\n      <td>-0.145990</td>\n      <td>-0.689599</td>\n      <td>0.727035</td>\n      <td>0.093808</td>\n      <td>0.613867</td>\n      <td>-0.156318</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.440064</td>\n      <td>0.455458</td>\n      <td>-0.893356</td>\n      <td>0.295065</td>\n      <td>0.160187</td>\n      <td>-0.527492</td>\n      <td>0.092013</td>\n      <td>-0.255349</td>\n      <td>0.982621</td>\n      <td>0.172117</td>\n      <td>...</td>\n      <td>-0.485759</td>\n      <td>-0.251611</td>\n      <td>0.203046</td>\n      <td>0.436484</td>\n      <td>0.584199</td>\n      <td>0.138801</td>\n      <td>0.179525</td>\n      <td>0.117047</td>\n      <td>-0.514392</td>\n      <td>-0.121610</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.088425</td>\n      <td>-0.284115</td>\n      <td>0.300834</td>\n      <td>-0.170313</td>\n      <td>-0.476598</td>\n      <td>-0.773967</td>\n      <td>0.929880</td>\n      <td>-0.669233</td>\n      <td>1.217801</td>\n      <td>-0.511516</td>\n      <td>...</td>\n      <td>-0.428810</td>\n      <td>-0.814650</td>\n      <td>0.189426</td>\n      <td>0.598014</td>\n      <td>-0.078981</td>\n      <td>0.344360</td>\n      <td>0.305921</td>\n      <td>0.111837</td>\n      <td>-0.194737</td>\n      <td>0.286152</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-0.585023</td>\n      <td>-0.932097</td>\n      <td>0.649800</td>\n      <td>-1.510238</td>\n      <td>0.908814</td>\n      <td>0.078908</td>\n      <td>-0.227934</td>\n      <td>-0.126206</td>\n      <td>-0.677687</td>\n      <td>-0.472893</td>\n      <td>...</td>\n      <td>-0.032966</td>\n      <td>-0.012548</td>\n      <td>1.557752</td>\n      <td>0.467225</td>\n      <td>0.275561</td>\n      <td>-0.823539</td>\n      <td>-0.176462</td>\n      <td>0.279183</td>\n      <td>-0.277266</td>\n      <td>-0.130289</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 768 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data12=pd.concat([data,cv1],axis=1)\ndata12['Labels']=full_df.Labels.values\ndata12['Length']=full_df.Length.values\ndata12['Length1']=full_df.Length1.values\ndata12.head()","execution_count":56,"outputs":[{"output_type":"execute_result","execution_count":56,"data":{"text/plain":"          0         1         2         3         4         5         6  \\\n0 -0.144368 -0.238294  0.599350  0.149775  0.170297 -0.141637  0.989932   \n1  0.296415 -0.713782 -0.306153 -0.011605  0.344352 -1.188767  0.150497   \n2 -0.440064  0.455458 -0.893356  0.295065  0.160187 -0.527492  0.092013   \n3  0.088425 -0.284115  0.300834 -0.170313 -0.476598 -0.773967  0.929880   \n4 -0.585023 -0.932097  0.649800 -1.510238  0.908814  0.078908 -0.227934   \n\n          7         8         9  ...  water  wealth  weather  week  welfare  \\\n0 -0.510203  0.628405  0.283468  ...      0       0        0     0        0   \n1 -0.162290  0.770730 -0.092008  ...      0       0        0     0        0   \n2 -0.255349  0.982621  0.172117  ...      0       0        0     0        0   \n3 -0.669233  1.217801 -0.511516  ...      0       0        0     0        0   \n4 -0.126206 -0.677687 -0.472893  ...      0       0        0     0        0   \n\n   women  workers  Labels  Length  Length1  \n0      0        0     1.0      82        8  \n1      0        0     2.0     141       34  \n2      0        0     3.0     105       14  \n3      0        0     1.0      78       11  \n4      0        0     2.0      54       12  \n\n[5 rows x 952 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>water</th>\n      <th>wealth</th>\n      <th>weather</th>\n      <th>week</th>\n      <th>welfare</th>\n      <th>women</th>\n      <th>workers</th>\n      <th>Labels</th>\n      <th>Length</th>\n      <th>Length1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-0.144368</td>\n      <td>-0.238294</td>\n      <td>0.599350</td>\n      <td>0.149775</td>\n      <td>0.170297</td>\n      <td>-0.141637</td>\n      <td>0.989932</td>\n      <td>-0.510203</td>\n      <td>0.628405</td>\n      <td>0.283468</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.0</td>\n      <td>82</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.296415</td>\n      <td>-0.713782</td>\n      <td>-0.306153</td>\n      <td>-0.011605</td>\n      <td>0.344352</td>\n      <td>-1.188767</td>\n      <td>0.150497</td>\n      <td>-0.162290</td>\n      <td>0.770730</td>\n      <td>-0.092008</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.0</td>\n   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<td>78</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-0.585023</td>\n      <td>-0.932097</td>\n      <td>0.649800</td>\n      <td>-1.510238</td>\n      <td>0.908814</td>\n      <td>0.078908</td>\n      <td>-0.227934</td>\n      <td>-0.126206</td>\n      <td>-0.677687</td>\n      <td>-0.472893</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>54</td>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 952 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train1 = data12[~data12.Labels.isna()]\ntest1 = data12[data12.Labels.isna()]\ntest1.drop(\"Labels\",axis=1,inplace=True)","execution_count":57,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:4167: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  errors=errors,\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=train1.drop(['Labels'],axis=1)\ny=train1['Labels']","execution_count":58,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)","execution_count":59,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"vclf.fit(X_train,y_train)","execution_count":60,"outputs":[{"output_type":"stream","text":"Learning rate set to 0.099662\n","name":"stdout"},{"output_type":"stream","text":"Warning: less than 75% gpu memory available for training. 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0.4344802\ttotal: 19.9s\tremaining: 181ms\n991:\tlearn: 0.4341725\ttotal: 19.9s\tremaining: 161ms\n992:\tlearn: 0.4337823\ttotal: 19.9s\tremaining: 140ms\n993:\tlearn: 0.4331188\ttotal: 19.9s\tremaining: 120ms\n994:\tlearn: 0.4325391\ttotal: 20s\tremaining: 100ms\n995:\tlearn: 0.4322003\ttotal: 20s\tremaining: 80.3ms\n996:\tlearn: 0.4316232\ttotal: 20s\tremaining: 60.2ms\n997:\tlearn: 0.4310748\ttotal: 20s\tremaining: 40.1ms\n998:\tlearn: 0.4307710\ttotal: 20s\tremaining: 20.1ms\n999:\tlearn: 0.4301979\ttotal: 20.1s\tremaining: 0us\n","name":"stdout"},{"output_type":"execute_result","execution_count":60,"data":{"text/plain":"VotingClassifier(estimators=[('lgg', LGBMClassifier(device='gpu', max_bin=25)),\n                             ('xgg',\n                              <catboost.core.CatBoostClassifier object at 0x7fb16feba2d0>)],\n                 voting='soft')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"prediction=vclf.predict_proba(test1)","execution_count":61,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Results are using bert large sentence transformer\nresults3=pd.DataFrame(prediction, columns=[0,1,2,3,4,5])","execution_count":62,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final=(results+results1+results2+results3)/4","execution_count":63,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final.head()","execution_count":64,"outputs":[{"output_type":"execute_result","execution_count":64,"data":{"text/plain":"          0         1         2         3         4         5\n0  0.184829  0.198724  0.181477  0.197726  0.041716  0.195528\n1  0.132969  0.233023  0.198231  0.259482  0.062737  0.113557\n2  0.137865  0.228555  0.157320  0.125697  0.101759  0.248804\n3  0.219476  0.319810  0.246131  0.110130  0.055268  0.049186\n4  0.148455  0.282205  0.196449  0.113686  0.118126  0.141079","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.184829</td>\n      <td>0.198724</td>\n      <td>0.181477</td>\n      <td>0.197726</td>\n      <td>0.041716</td>\n      <td>0.195528</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.132969</td>\n      <td>0.233023</td>\n      <td>0.198231</td>\n      <td>0.259482</td>\n      <td>0.062737</td>\n      <td>0.113557</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.137865</td>\n      <td>0.228555</td>\n      <td>0.157320</td>\n      <td>0.125697</td>\n      <td>0.101759</td>\n      <td>0.248804</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.219476</td>\n      <td>0.319810</td>\n      <td>0.246131</td>\n      <td>0.110130</td>\n      <td>0.055268</td>\n      <td>0.049186</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.148455</td>\n      <td>0.282205</td>\n      <td>0.196449</td>\n      <td>0.113686</td>\n      <td>0.118126</td>\n      <td>0.141079</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"final.to_excel('/kaggle/working/final.xlsx',index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}